ANCOVA in
Vocabulary taught (Vocabulary taught)
Geiser C. Challco geiser@alumni.usp.br
NOTE:
- Teste ANCOVA para determinar se houve diferenças significativas no
Vocabulary taught (medido usando pre- e pos-testes).
- ANCOVA test to determine whether there were significant differences
in Vocabulary taught (measured using pre- and post-tests).
Setting Initial Variables
dv = "score.vocab.ensinado"
dv.pos = "score.vocab.ensinado.pos"
dv.pre = "score.vocab.ensinado.pre"
fatores2 <- c("genero","zona.participante","zona.escola","score.vocab.ensinado.quintile")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2
fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1
lfatores <- c(lfatores1)
color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#008000")
color[["genero"]] = c("#FF007F","#4D4DFF")
color[["zona.escola"]] = c("#AA00FF","#00CCCC")
color[["zona.participante"]] = c("#AA00FF","#00CCCC")
level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["genero"]] = c("F","M")
level[["zona.escola"]] = c("Rural","Urbana")
level[["zona.participante"]] = c("Rural","Urbana")
# ..
ymin <- 0
ymax <- 0
ymin.ci <- 0
ymax.ci <- 0
color[["grupo:genero"]] = c(
"Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
"Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
"Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
"Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:zona.escola"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:zona.participante"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
for (coln in c(
"palavras.lidas","score.compreensao","tri.compreensao",
"score.vocab","tri.vocab",
"score.vocab.ensinado","tri.vocab.ensinado","score.vocab.nao.ensinado","tri.vocab.nao.ensinado",
"score.CLPP","tri.CLPP","score.CR","tri.CR",
"score.CI","tri.CI","score.TV","tri.TV","score.TF","tri.TF","score.TO","tri.TO")) {
color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
color[[paste0("grupo:",coln,".quintile")]] = c(
"Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
"Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
"Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
"Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
"Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
"Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
"Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
"Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
"Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
"Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}
gdat <- read_excel("../data/data.xlsx", sheet = "vocabulario.wg.wo.st")
dat <- gdat
dat$grupo <- factor(dat[["grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]
dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])
for (f in c("grupo", names(lfatores))) {
if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0)
color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}
ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()
Descriptive Statistics
of Initial Data
df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
score.vocab.ensinado.pre |
176 |
3.807 |
4.0 |
0 |
10 |
2.327 |
0.175 |
0.346 |
3.00 |
YES |
0.328 |
-0.438 |
| Experimental |
|
|
|
|
score.vocab.ensinado.pre |
135 |
4.104 |
4.0 |
0 |
10 |
2.180 |
0.188 |
0.371 |
3.00 |
YES |
0.358 |
-0.067 |
|
|
|
|
|
score.vocab.ensinado.pre |
311 |
3.936 |
4.0 |
0 |
10 |
2.265 |
0.128 |
0.253 |
3.00 |
YES |
0.327 |
-0.275 |
| Controle |
|
|
|
|
score.vocab.ensinado.pos |
176 |
3.795 |
4.0 |
0 |
10 |
2.276 |
0.172 |
0.339 |
4.00 |
YES |
0.228 |
-0.735 |
| Experimental |
|
|
|
|
score.vocab.ensinado.pos |
135 |
4.215 |
4.0 |
0 |
10 |
2.389 |
0.206 |
0.407 |
3.00 |
YES |
0.047 |
-0.472 |
|
|
|
|
|
score.vocab.ensinado.pos |
311 |
3.977 |
4.0 |
0 |
10 |
2.331 |
0.132 |
0.260 |
4.00 |
YES |
0.155 |
-0.606 |
| Controle |
F |
|
|
|
score.vocab.ensinado.pre |
87 |
3.954 |
4.0 |
0 |
10 |
2.401 |
0.257 |
0.512 |
3.00 |
YES |
0.437 |
-0.336 |
| Controle |
M |
|
|
|
score.vocab.ensinado.pre |
89 |
3.663 |
4.0 |
0 |
9 |
2.256 |
0.239 |
0.475 |
3.00 |
YES |
0.171 |
-0.750 |
| Experimental |
F |
|
|
|
score.vocab.ensinado.pre |
59 |
4.576 |
4.0 |
0 |
10 |
2.313 |
0.301 |
0.603 |
3.00 |
YES |
0.479 |
-0.187 |
| Experimental |
M |
|
|
|
score.vocab.ensinado.pre |
75 |
3.680 |
4.0 |
0 |
8 |
1.960 |
0.226 |
0.451 |
3.00 |
YES |
0.020 |
-0.671 |
| Experimental |
|
|
|
|
score.vocab.ensinado.pre |
1 |
8.000 |
8.0 |
8 |
8 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Controle |
F |
|
|
|
score.vocab.ensinado.pos |
87 |
4.057 |
4.0 |
0 |
10 |
2.364 |
0.253 |
0.504 |
4.00 |
YES |
0.224 |
-0.888 |
| Controle |
M |
|
|
|
score.vocab.ensinado.pos |
89 |
3.539 |
3.0 |
0 |
9 |
2.169 |
0.230 |
0.457 |
3.00 |
YES |
0.172 |
-0.724 |
| Experimental |
F |
|
|
|
score.vocab.ensinado.pos |
59 |
4.542 |
5.0 |
0 |
10 |
2.514 |
0.327 |
0.655 |
3.00 |
YES |
-0.139 |
-0.643 |
| Experimental |
M |
|
|
|
score.vocab.ensinado.pos |
75 |
4.013 |
4.0 |
0 |
10 |
2.239 |
0.259 |
0.515 |
2.00 |
YES |
0.183 |
-0.236 |
| Experimental |
|
|
|
|
score.vocab.ensinado.pos |
1 |
0.000 |
0.0 |
0 |
0 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Controle |
|
Rural |
|
|
score.vocab.ensinado.pre |
56 |
3.696 |
3.0 |
0 |
10 |
2.536 |
0.339 |
0.679 |
3.00 |
NO |
0.598 |
-0.231 |
| Controle |
|
Urbana |
|
|
score.vocab.ensinado.pre |
77 |
3.896 |
4.0 |
0 |
9 |
2.303 |
0.262 |
0.523 |
3.00 |
YES |
0.074 |
-0.796 |
| Controle |
|
|
|
|
score.vocab.ensinado.pre |
43 |
3.791 |
4.0 |
0 |
9 |
2.122 |
0.324 |
0.653 |
3.00 |
YES |
0.312 |
-0.471 |
| Experimental |
|
Rural |
|
|
score.vocab.ensinado.pre |
52 |
3.808 |
4.0 |
0 |
10 |
2.232 |
0.310 |
0.621 |
3.00 |
NO |
0.517 |
-0.054 |
| Experimental |
|
Urbana |
|
|
score.vocab.ensinado.pre |
49 |
4.163 |
4.0 |
0 |
10 |
2.115 |
0.302 |
0.607 |
3.00 |
YES |
0.269 |
-0.192 |
| Experimental |
|
|
|
|
score.vocab.ensinado.pre |
34 |
4.471 |
4.5 |
0 |
10 |
2.191 |
0.376 |
0.765 |
1.75 |
YES |
0.248 |
-0.058 |
| Controle |
|
Rural |
|
|
score.vocab.ensinado.pos |
56 |
4.161 |
4.0 |
1 |
8 |
1.980 |
0.265 |
0.530 |
3.00 |
YES |
0.444 |
-0.978 |
| Controle |
|
Urbana |
|
|
score.vocab.ensinado.pos |
77 |
3.935 |
4.0 |
0 |
10 |
2.478 |
0.282 |
0.562 |
4.00 |
YES |
0.171 |
-0.821 |
| Controle |
|
|
|
|
score.vocab.ensinado.pos |
43 |
3.070 |
3.0 |
0 |
7 |
2.142 |
0.327 |
0.659 |
3.50 |
YES |
0.195 |
-1.203 |
| Experimental |
|
Rural |
|
|
score.vocab.ensinado.pos |
52 |
4.712 |
4.0 |
1 |
9 |
1.934 |
0.268 |
0.538 |
3.00 |
YES |
0.370 |
-0.430 |
| Experimental |
|
Urbana |
|
|
score.vocab.ensinado.pos |
49 |
4.286 |
4.0 |
0 |
10 |
2.458 |
0.351 |
0.706 |
3.00 |
YES |
-0.126 |
-0.737 |
| Experimental |
|
|
|
|
score.vocab.ensinado.pos |
34 |
3.353 |
3.0 |
0 |
10 |
2.729 |
0.468 |
0.952 |
3.75 |
YES |
0.498 |
-0.493 |
| Controle |
|
|
Rural |
|
score.vocab.ensinado.pre |
58 |
3.897 |
4.0 |
0 |
9 |
2.330 |
0.306 |
0.613 |
2.75 |
YES |
0.246 |
-0.616 |
| Controle |
|
|
Urbana |
|
score.vocab.ensinado.pre |
118 |
3.763 |
4.0 |
0 |
10 |
2.334 |
0.215 |
0.426 |
3.00 |
YES |
0.365 |
-0.385 |
| Experimental |
|
|
Rural |
|
score.vocab.ensinado.pre |
40 |
4.350 |
4.5 |
0 |
10 |
2.497 |
0.395 |
0.798 |
3.25 |
YES |
0.487 |
-0.441 |
| Experimental |
|
|
Urbana |
|
score.vocab.ensinado.pre |
95 |
4.000 |
4.0 |
0 |
10 |
2.037 |
0.209 |
0.415 |
2.00 |
YES |
0.172 |
-0.186 |
| Controle |
|
|
Rural |
|
score.vocab.ensinado.pos |
58 |
4.052 |
3.5 |
0 |
8 |
2.164 |
0.284 |
0.569 |
4.00 |
YES |
0.323 |
-1.087 |
| Controle |
|
|
Urbana |
|
score.vocab.ensinado.pos |
118 |
3.669 |
4.0 |
0 |
10 |
2.328 |
0.214 |
0.424 |
3.00 |
YES |
0.214 |
-0.673 |
| Experimental |
|
|
Rural |
|
score.vocab.ensinado.pos |
40 |
4.300 |
4.0 |
0 |
10 |
2.267 |
0.358 |
0.725 |
2.00 |
YES |
0.332 |
-0.063 |
| Experimental |
|
|
Urbana |
|
score.vocab.ensinado.pos |
95 |
4.179 |
4.0 |
0 |
10 |
2.449 |
0.251 |
0.499 |
3.00 |
YES |
-0.042 |
-0.678 |
| Controle |
|
|
|
1st quintile |
score.vocab.ensinado.pre |
32 |
0.562 |
1.0 |
0 |
1 |
0.504 |
0.089 |
0.182 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
2nd quintile |
score.vocab.ensinado.pre |
50 |
2.600 |
3.0 |
2 |
3 |
0.495 |
0.070 |
0.141 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
3rd quintile |
score.vocab.ensinado.pre |
71 |
4.789 |
5.0 |
4 |
6 |
0.791 |
0.094 |
0.187 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
4th quintile |
score.vocab.ensinado.pre |
17 |
7.471 |
7.0 |
7 |
8 |
0.514 |
0.125 |
0.265 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
5th quintile |
score.vocab.ensinado.pre |
6 |
9.167 |
9.0 |
9 |
10 |
0.408 |
0.167 |
0.428 |
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
1st quintile |
score.vocab.ensinado.pre |
15 |
0.667 |
1.0 |
0 |
1 |
0.488 |
0.126 |
0.270 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
2nd quintile |
score.vocab.ensinado.pre |
37 |
2.459 |
2.0 |
2 |
3 |
0.505 |
0.083 |
0.168 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
3rd quintile |
score.vocab.ensinado.pre |
65 |
4.785 |
5.0 |
4 |
6 |
0.760 |
0.094 |
0.188 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
4th quintile |
score.vocab.ensinado.pre |
14 |
7.357 |
7.0 |
7 |
8 |
0.497 |
0.133 |
0.287 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
5th quintile |
score.vocab.ensinado.pre |
4 |
9.750 |
10.0 |
9 |
10 |
0.500 |
0.250 |
0.796 |
0.25 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
1st quintile |
score.vocab.ensinado.pos |
32 |
2.781 |
2.0 |
0 |
8 |
1.930 |
0.341 |
0.696 |
2.00 |
NO |
0.692 |
0.070 |
| Controle |
|
|
|
2nd quintile |
score.vocab.ensinado.pos |
50 |
2.940 |
3.0 |
0 |
8 |
2.064 |
0.292 |
0.587 |
2.75 |
NO |
0.733 |
-0.284 |
| Controle |
|
|
|
3rd quintile |
score.vocab.ensinado.pos |
71 |
4.070 |
4.0 |
0 |
9 |
2.024 |
0.240 |
0.479 |
2.00 |
YES |
-0.074 |
-0.377 |
| Controle |
|
|
|
4th quintile |
score.vocab.ensinado.pos |
17 |
6.118 |
6.0 |
0 |
10 |
2.233 |
0.542 |
1.148 |
2.00 |
NO |
-0.963 |
1.182 |
| Controle |
|
|
|
5th quintile |
score.vocab.ensinado.pos |
6 |
6.500 |
6.5 |
5 |
8 |
1.049 |
0.428 |
1.101 |
1.00 |
YES |
0.000 |
-1.571 |
| Experimental |
|
|
|
1st quintile |
score.vocab.ensinado.pos |
15 |
3.467 |
4.0 |
0 |
7 |
2.066 |
0.533 |
1.144 |
3.00 |
YES |
-0.047 |
-1.346 |
| Experimental |
|
|
|
2nd quintile |
score.vocab.ensinado.pos |
37 |
3.919 |
4.0 |
0 |
8 |
2.073 |
0.341 |
0.691 |
2.00 |
YES |
-0.150 |
-0.313 |
| Experimental |
|
|
|
3rd quintile |
score.vocab.ensinado.pos |
65 |
3.969 |
4.0 |
0 |
9 |
2.291 |
0.284 |
0.568 |
4.00 |
YES |
-0.047 |
-0.719 |
| Experimental |
|
|
|
4th quintile |
score.vocab.ensinado.pos |
14 |
5.929 |
6.0 |
0 |
10 |
2.702 |
0.722 |
1.560 |
3.25 |
NO |
-0.603 |
-0.449 |
| Experimental |
|
|
|
5th quintile |
score.vocab.ensinado.pos |
4 |
7.750 |
7.5 |
6 |
10 |
2.062 |
1.031 |
3.280 |
3.25 |
few data |
0.000 |
0.000 |
ANCOVA and Pairwise
for one factor: grupo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "score.vocab.ensinado.pos", "grupo")
pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.vocab.ensinado"]] <- c(pdat[["score.vocab.ensinado.pre"]], pdat[["score.vocab.ensinado.pos"]])
aov = anova_test(pdat, score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, score.vocab.ensinado.pos ~ grupo, covariate = score.vocab.ensinado.pre,
p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
score.vocab.ensinado ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "score.vocab.ensinado.pos", "grupo", covar = "score.vocab.ensinado.pre")
ds <- merge(ds[ds$variable != "score.vocab.ensinado.pre",],
ds[ds$variable == "score.vocab.ensinado.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".score.vocab.ensinado.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.score.vocab.ensinado.pre","se.score.vocab.ensinado.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- ds
Computing
ANCOVA and PairWise After removing non-normal data (OK)
wdat = pdat
res = residuals(lm(score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.vocab.ensinado"]] <- c(wdat[["score.vocab.ensinado.pre"]], wdat[["score.vocab.ensinado.pos"]])
ldat[["grupo"]] = wdat
(non.normal)
## NULL
aov = anova_test(wdat, score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
by="Effect", suffixes = c("","'"))
(df = get_anova_table(aov))
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 score.vocab.ensinado.pre 1 308 65.187 1.55e-14 * 0.175
## 2 grupo 1 308 1.447 2.30e-01 0.005
| score.vocab.ensinado.pre |
1 |
308 |
65.187 |
0.00 |
* |
0.175 |
| grupo |
1 |
308 |
1.447 |
0.23 |
|
0.005 |
pwc <- emmeans_test(wdat, score.vocab.ensinado.pos ~ grupo, covariate = score.vocab.ensinado.pre,
p.adjust.method = "bonferroni")
| score.vocab.ensinado.pre*grupo |
score.vocab.ensinado.pos |
Controle |
Experimental |
308 |
-1.203 |
0.23 |
0.23 |
ns |
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
score.vocab.ensinado ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
by=c("grupo","term",".y.","group1","group2"),
suffixes = c("","'"))
| Controle |
time |
score.vocab.ensinado |
pre |
pos |
618 |
0.046 |
0.963 |
0.963 |
ns |
| Experimental |
time |
score.vocab.ensinado |
pre |
pos |
618 |
-0.398 |
0.691 |
0.691 |
ns |
ds <- get.descriptives(wdat, "score.vocab.ensinado.pos", "grupo", covar = "score.vocab.ensinado.pre")
ds <- merge(ds[ds$variable != "score.vocab.ensinado.pre",],
ds[ds$variable == "score.vocab.ensinado.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".score.vocab.ensinado.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.score.vocab.ensinado.pre","se.score.vocab.ensinado.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
| Controle |
176 |
3.807 |
0.175 |
3.795 |
0.172 |
3.851 |
0.160 |
3.537 |
4.165 |
| Experimental |
135 |
4.104 |
0.188 |
4.215 |
0.206 |
4.143 |
0.182 |
3.784 |
4.502 |
Plots for ancova
plots <- oneWayAncovaPlots(
wdat, "score.vocab.ensinado.pos", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
font.label.size=10, step.increase=0.05, p.label="p.adj",
subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
plots[["grupo"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
wdat, "score.vocab.ensinado.pos", "grupo", aov, pwc, covar = "score.vocab.ensinado.pre",
theme = "classic", color = color[["grupo"]],
subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
plots <- oneWayAncovaBoxPlots(
wdat.long, "score.vocab.ensinado", "grupo", aov, pwc.long,
pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
color = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking normality and
homogeneity
res <- augment(lm(score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo, data = wdat))
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.997 0.733
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 1 309 2.84 0.0931
ANCOVA and
Pairwise for two factors grupo:genero
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["genero"]]),],
"score.vocab.ensinado.pos", c("grupo","genero"))
pdat = pdat[pdat[["genero"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["genero"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["genero"]] = factor(
pdat[["genero"]],
level[["genero"]][level[["genero"]] %in% unique(pdat[["genero"]])])
pdat.long <- rbind(pdat[,c("id","grupo","genero")], pdat[,c("id","grupo","genero")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.vocab.ensinado"]] <- c(pdat[["score.vocab.ensinado.pre"]], pdat[["score.vocab.ensinado.pos"]])
if (length(unique(pdat[["genero"]])) >= 2) {
aov = anova_test(pdat, score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*genero)
laov[["grupo:genero"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["genero"]])) >= 2) {
pwcs <- list()
pwcs[["genero"]] <- emmeans_test(
group_by(pdat, grupo), score.vocab.ensinado.pos ~ genero,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, genero), score.vocab.ensinado.pos ~ grupo,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["genero"]])
pwc <- pwc[,c("grupo","genero", colnames(pwc)[!colnames(pwc) %in% c("grupo","genero")])]
}
if (length(unique(pdat[["genero"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","genero")),
score.vocab.ensinado ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:genero"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["genero"]])) >= 2) {
ds <- get.descriptives(pdat, "score.vocab.ensinado.pos", c("grupo","genero"), covar = "score.vocab.ensinado.pre")
ds <- merge(ds[ds$variable != "score.vocab.ensinado.pre",],
ds[ds$variable == "score.vocab.ensinado.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","genero"), all.x = T, suffixes = c("", ".score.vocab.ensinado.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","genero"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","genero","n","mean.score.vocab.ensinado.pre","se.score.vocab.ensinado.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","genero", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:genero"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["genero"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*genero, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","genero")], wdat[,c("id","grupo","genero")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.vocab.ensinado"]] <- c(wdat[["score.vocab.ensinado.pre"]], wdat[["score.vocab.ensinado.pos"]])
ldat[["grupo:genero"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["genero"]])) >= 2) {
aov = anova_test(wdat, score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*genero)
laov[["grupo:genero"]] <- merge(get_anova_table(aov), laov[["grupo:genero"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.vocab.ensinado.pre |
1 |
305 |
67.423 |
0.000 |
* |
0.181 |
| grupo |
1 |
305 |
2.101 |
0.148 |
|
0.007 |
| genero |
1 |
305 |
1.382 |
0.241 |
|
0.005 |
| grupo:genero |
1 |
305 |
0.277 |
0.599 |
|
0.001 |
if (length(unique(pdat[["genero"]])) >= 2) {
pwcs <- list()
pwcs[["genero"]] <- emmeans_test(
group_by(wdat, grupo), score.vocab.ensinado.pos ~ genero,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, genero), score.vocab.ensinado.pos ~ grupo,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["genero"]])
pwc <- pwc[,c("grupo","genero", colnames(pwc)[!colnames(pwc) %in% c("grupo","genero")])]
}
|
F |
score.vocab.ensinado.pre*grupo |
score.vocab.ensinado.pos |
Controle |
Experimental |
305 |
-0.598 |
0.550 |
0.550 |
ns |
|
M |
score.vocab.ensinado.pre*grupo |
score.vocab.ensinado.pos |
Controle |
Experimental |
305 |
-1.422 |
0.156 |
0.156 |
ns |
| Controle |
|
score.vocab.ensinado.pre*genero |
score.vocab.ensinado.pos |
F |
M |
305 |
1.236 |
0.217 |
0.217 |
ns |
| Experimental |
|
score.vocab.ensinado.pre*genero |
score.vocab.ensinado.pos |
F |
M |
305 |
0.371 |
0.711 |
0.711 |
ns |
if (length(unique(pdat[["genero"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","genero")),
score.vocab.ensinado ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:genero"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:genero"]],
by=c("grupo","genero","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
F |
time |
score.vocab.ensinado |
pre |
pos |
612 |
-0.300 |
0.764 |
0.764 |
ns |
| Controle |
M |
time |
score.vocab.ensinado |
pre |
pos |
612 |
0.362 |
0.717 |
0.717 |
ns |
| Experimental |
F |
time |
score.vocab.ensinado |
pre |
pos |
612 |
0.081 |
0.936 |
0.936 |
ns |
| Experimental |
M |
time |
score.vocab.ensinado |
pre |
pos |
612 |
-0.897 |
0.370 |
0.370 |
ns |
if (length(unique(pdat[["genero"]])) >= 2) {
ds <- get.descriptives(wdat, "score.vocab.ensinado.pos", c("grupo","genero"), covar = "score.vocab.ensinado.pre")
ds <- merge(ds[ds$variable != "score.vocab.ensinado.pre",],
ds[ds$variable == "score.vocab.ensinado.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","genero"), all.x = T, suffixes = c("", ".score.vocab.ensinado.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","genero"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","genero","n","mean.score.vocab.ensinado.pre","se.score.vocab.ensinado.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","genero", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:genero"]] <- merge(ds, lemms[["grupo:genero"]],
by=c("grupo","genero"), suffixes = c("","'"))
}
| Controle |
F |
87 |
3.954 |
0.257 |
4.057 |
0.253 |
4.044 |
0.224 |
3.602 |
4.485 |
| Controle |
M |
89 |
3.663 |
0.239 |
3.539 |
0.230 |
3.653 |
0.222 |
3.216 |
4.091 |
| Experimental |
F |
59 |
4.576 |
0.301 |
4.542 |
0.327 |
4.256 |
0.275 |
3.715 |
4.797 |
| Experimental |
M |
75 |
3.680 |
0.226 |
4.013 |
0.259 |
4.120 |
0.242 |
3.643 |
4.596 |
Plots for ancova
if (length(unique(pdat[["genero"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "genero", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:genero"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["genero"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["genero"]])) >= 2) {
ggPlotAoC2(pwcs, "genero", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:genero"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["genero"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.vocab.ensinado.pos", c("grupo","genero"), aov, pwcs, covar = "score.vocab.ensinado.pre",
theme = "classic", color = color[["grupo:genero"]],
subtitle = which(aov$Effect == "grupo:genero"))
}
if (length(unique(pdat[["genero"]])) >= 2) {
plots[["grupo:genero"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["genero"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.vocab.ensinado", c("grupo","genero"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["genero"]])) >= 2)
plots[["grupo:genero"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
facet.by = c("grupo","genero"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
color = "grupo", facet.by = "genero", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:genero"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
color = "genero", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = genero)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:genero"))) +
ggplot2::scale_color_manual(values = color[["genero"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["genero"]])) >= 2)
res <- augment(lm(score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*genero, data = wdat))
if (length(unique(pdat[["genero"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.996 0.709
if (length(unique(pdat[["genero"]])) >= 2)
levene_test(res, .resid ~ grupo*genero)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 306 1.43 0.235
ANCOVA
and Pairwise for two factors
grupo:zona.participante
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["zona.participante"]]),],
"score.vocab.ensinado.pos", c("grupo","zona.participante"))
pdat = pdat[pdat[["zona.participante"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["zona.participante"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["zona.participante"]] = factor(
pdat[["zona.participante"]],
level[["zona.participante"]][level[["zona.participante"]] %in% unique(pdat[["zona.participante"]])])
pdat.long <- rbind(pdat[,c("id","grupo","zona.participante")], pdat[,c("id","grupo","zona.participante")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.vocab.ensinado"]] <- c(pdat[["score.vocab.ensinado.pre"]], pdat[["score.vocab.ensinado.pos"]])
if (length(unique(pdat[["zona.participante"]])) >= 2) {
aov = anova_test(pdat, score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*zona.participante)
laov[["grupo:zona.participante"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwcs <- list()
pwcs[["zona.participante"]] <- emmeans_test(
group_by(pdat, grupo), score.vocab.ensinado.pos ~ zona.participante,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, zona.participante), score.vocab.ensinado.pos ~ grupo,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.participante"]])
pwc <- pwc[,c("grupo","zona.participante", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.participante")])]
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","zona.participante")),
score.vocab.ensinado ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.participante"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ds <- get.descriptives(pdat, "score.vocab.ensinado.pos", c("grupo","zona.participante"), covar = "score.vocab.ensinado.pre")
ds <- merge(ds[ds$variable != "score.vocab.ensinado.pre",],
ds[ds$variable == "score.vocab.ensinado.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.participante"), all.x = T, suffixes = c("", ".score.vocab.ensinado.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.participante"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.participante","n","mean.score.vocab.ensinado.pre","se.score.vocab.ensinado.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.participante", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.participante"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["zona.participante"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*zona.participante, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","zona.participante")], wdat[,c("id","grupo","zona.participante")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.vocab.ensinado"]] <- c(wdat[["score.vocab.ensinado.pre"]], wdat[["score.vocab.ensinado.pos"]])
ldat[["grupo:zona.participante"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["zona.participante"]])) >= 2) {
aov = anova_test(wdat, score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*zona.participante)
laov[["grupo:zona.participante"]] <- merge(get_anova_table(aov), laov[["grupo:zona.participante"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.vocab.ensinado.pre |
1 |
229 |
57.816 |
0.000 |
* |
0.202 |
| grupo |
1 |
229 |
1.827 |
0.178 |
|
0.008 |
| zona.participante |
1 |
229 |
2.623 |
0.107 |
|
0.011 |
| grupo:zona.participante |
1 |
229 |
0.252 |
0.616 |
|
0.001 |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwcs <- list()
pwcs[["zona.participante"]] <- emmeans_test(
group_by(wdat, grupo), score.vocab.ensinado.pos ~ zona.participante,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, zona.participante), score.vocab.ensinado.pos ~ grupo,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.participante"]])
pwc <- pwc[,c("grupo","zona.participante", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.participante")])]
}
|
Rural |
score.vocab.ensinado.pre*grupo |
score.vocab.ensinado.pos |
Controle |
Experimental |
229 |
-1.295 |
0.197 |
0.197 |
ns |
|
Urbana |
score.vocab.ensinado.pre*grupo |
score.vocab.ensinado.pos |
Controle |
Experimental |
229 |
-0.635 |
0.526 |
0.526 |
ns |
| Controle |
|
score.vocab.ensinado.pre*zona.participante |
score.vocab.ensinado.pos |
Rural |
Urbana |
229 |
0.885 |
0.377 |
0.377 |
ns |
| Experimental |
|
score.vocab.ensinado.pre*zona.participante |
score.vocab.ensinado.pos |
Rural |
Urbana |
229 |
1.448 |
0.149 |
0.149 |
ns |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","zona.participante")),
score.vocab.ensinado ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.participante"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:zona.participante"]],
by=c("grupo","zona.participante","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
score.vocab.ensinado |
pre |
pos |
460 |
-1.078 |
0.282 |
0.282 |
ns |
| Controle |
Urbana |
time |
score.vocab.ensinado |
pre |
pos |
460 |
-0.106 |
0.916 |
0.916 |
ns |
| Experimental |
Rural |
time |
score.vocab.ensinado |
pre |
pos |
460 |
-2.022 |
0.044 |
0.044 |
* |
| Experimental |
Urbana |
time |
score.vocab.ensinado |
pre |
pos |
460 |
-0.266 |
0.790 |
0.790 |
ns |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ds <- get.descriptives(wdat, "score.vocab.ensinado.pos", c("grupo","zona.participante"), covar = "score.vocab.ensinado.pre")
ds <- merge(ds[ds$variable != "score.vocab.ensinado.pre",],
ds[ds$variable == "score.vocab.ensinado.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.participante"), all.x = T, suffixes = c("", ".score.vocab.ensinado.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.participante"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.participante","n","mean.score.vocab.ensinado.pre","se.score.vocab.ensinado.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.participante", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.participante"]] <- merge(ds, lemms[["grupo:zona.participante"]],
by=c("grupo","zona.participante"), suffixes = c("","'"))
}
| Controle |
Rural |
56 |
3.696 |
0.339 |
4.161 |
0.265 |
4.243 |
0.269 |
3.712 |
4.774 |
| Controle |
Urbana |
77 |
3.896 |
0.262 |
3.935 |
0.282 |
3.930 |
0.229 |
3.478 |
4.382 |
| Experimental |
Rural |
52 |
3.808 |
0.310 |
4.712 |
0.268 |
4.745 |
0.279 |
4.195 |
5.295 |
| Experimental |
Urbana |
49 |
4.163 |
0.302 |
4.286 |
0.351 |
4.164 |
0.288 |
3.596 |
4.732 |
Plots for ancova
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "zona.participante", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:zona.participante"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["zona.participante"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggPlotAoC2(pwcs, "zona.participante", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:zona.participante"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.vocab.ensinado.pos", c("grupo","zona.participante"), aov, pwcs, covar = "score.vocab.ensinado.pre",
theme = "classic", color = color[["grupo:zona.participante"]],
subtitle = which(aov$Effect == "grupo:zona.participante"))
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots[["grupo:zona.participante"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.vocab.ensinado", c("grupo","zona.participante"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["zona.participante"]])) >= 2)
plots[["grupo:zona.participante"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
facet.by = c("grupo","zona.participante"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
color = "grupo", facet.by = "zona.participante", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.participante"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
color = "zona.participante", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = zona.participante)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.participante"))) +
ggplot2::scale_color_manual(values = color[["zona.participante"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["zona.participante"]])) >= 2)
res <- augment(lm(score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*zona.participante, data = wdat))
if (length(unique(pdat[["zona.participante"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.995 0.604
if (length(unique(pdat[["zona.participante"]])) >= 2)
levene_test(res, .resid ~ grupo*zona.participante)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 230 2.41 0.0675
ANCOVA and
Pairwise for two factors grupo:zona.escola
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["zona.escola"]]),],
"score.vocab.ensinado.pos", c("grupo","zona.escola"))
pdat = pdat[pdat[["zona.escola"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["zona.escola"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["zona.escola"]] = factor(
pdat[["zona.escola"]],
level[["zona.escola"]][level[["zona.escola"]] %in% unique(pdat[["zona.escola"]])])
pdat.long <- rbind(pdat[,c("id","grupo","zona.escola")], pdat[,c("id","grupo","zona.escola")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.vocab.ensinado"]] <- c(pdat[["score.vocab.ensinado.pre"]], pdat[["score.vocab.ensinado.pos"]])
if (length(unique(pdat[["zona.escola"]])) >= 2) {
aov = anova_test(pdat, score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*zona.escola)
laov[["grupo:zona.escola"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwcs <- list()
pwcs[["zona.escola"]] <- emmeans_test(
group_by(pdat, grupo), score.vocab.ensinado.pos ~ zona.escola,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, zona.escola), score.vocab.ensinado.pos ~ grupo,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.escola"]])
pwc <- pwc[,c("grupo","zona.escola", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.escola")])]
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","zona.escola")),
score.vocab.ensinado ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.escola"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ds <- get.descriptives(pdat, "score.vocab.ensinado.pos", c("grupo","zona.escola"), covar = "score.vocab.ensinado.pre")
ds <- merge(ds[ds$variable != "score.vocab.ensinado.pre",],
ds[ds$variable == "score.vocab.ensinado.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.escola"), all.x = T, suffixes = c("", ".score.vocab.ensinado.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.escola"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.escola","n","mean.score.vocab.ensinado.pre","se.score.vocab.ensinado.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.escola", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.escola"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["zona.escola"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*zona.escola, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","zona.escola")], wdat[,c("id","grupo","zona.escola")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.vocab.ensinado"]] <- c(wdat[["score.vocab.ensinado.pre"]], wdat[["score.vocab.ensinado.pos"]])
ldat[["grupo:zona.escola"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["zona.escola"]])) >= 2) {
aov = anova_test(wdat, score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*zona.escola)
laov[["grupo:zona.escola"]] <- merge(get_anova_table(aov), laov[["grupo:zona.escola"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.vocab.ensinado.pre |
1 |
306 |
64.520 |
0.000 |
* |
0.174 |
| grupo |
1 |
306 |
1.504 |
0.221 |
|
0.005 |
| zona.escola |
1 |
306 |
0.464 |
0.496 |
|
0.002 |
| grupo:zona.escola |
1 |
306 |
0.455 |
0.501 |
|
0.001 |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwcs <- list()
pwcs[["zona.escola"]] <- emmeans_test(
group_by(wdat, grupo), score.vocab.ensinado.pos ~ zona.escola,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, zona.escola), score.vocab.ensinado.pos ~ grupo,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.escola"]])
pwc <- pwc[,c("grupo","zona.escola", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.escola")])]
}
|
Rural |
score.vocab.ensinado.pre*grupo |
score.vocab.ensinado.pos |
Controle |
Experimental |
306 |
-0.124 |
0.902 |
0.902 |
ns |
|
Urbana |
score.vocab.ensinado.pre*grupo |
score.vocab.ensinado.pos |
Controle |
Experimental |
306 |
-1.394 |
0.164 |
0.164 |
ns |
| Controle |
|
score.vocab.ensinado.pre*zona.escola |
score.vocab.ensinado.pos |
Rural |
Urbana |
306 |
0.955 |
0.340 |
0.340 |
ns |
| Experimental |
|
score.vocab.ensinado.pre*zona.escola |
score.vocab.ensinado.pos |
Rural |
Urbana |
306 |
-0.072 |
0.942 |
0.942 |
ns |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","zona.escola")),
score.vocab.ensinado ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.escola"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:zona.escola"]],
by=c("grupo","zona.escola","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
score.vocab.ensinado |
pre |
pos |
614 |
-0.363 |
0.716 |
0.716 |
ns |
| Controle |
Urbana |
time |
score.vocab.ensinado |
pre |
pos |
614 |
0.311 |
0.756 |
0.756 |
ns |
| Experimental |
Rural |
time |
score.vocab.ensinado |
pre |
pos |
614 |
0.097 |
0.923 |
0.923 |
ns |
| Experimental |
Urbana |
time |
score.vocab.ensinado |
pre |
pos |
614 |
-0.536 |
0.592 |
0.592 |
ns |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ds <- get.descriptives(wdat, "score.vocab.ensinado.pos", c("grupo","zona.escola"), covar = "score.vocab.ensinado.pre")
ds <- merge(ds[ds$variable != "score.vocab.ensinado.pre",],
ds[ds$variable == "score.vocab.ensinado.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.escola"), all.x = T, suffixes = c("", ".score.vocab.ensinado.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.escola"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.escola","n","mean.score.vocab.ensinado.pre","se.score.vocab.ensinado.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.escola", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.escola"]] <- merge(ds, lemms[["grupo:zona.escola"]],
by=c("grupo","zona.escola"), suffixes = c("","'"))
}
| Controle |
Rural |
58 |
3.897 |
0.306 |
4.052 |
0.284 |
4.068 |
0.278 |
3.521 |
4.616 |
| Controle |
Urbana |
118 |
3.763 |
0.215 |
3.669 |
0.214 |
3.744 |
0.195 |
3.359 |
4.128 |
| Experimental |
Rural |
40 |
4.350 |
0.395 |
4.300 |
0.358 |
4.122 |
0.336 |
3.461 |
4.784 |
| Experimental |
Urbana |
95 |
4.000 |
0.209 |
4.179 |
0.251 |
4.151 |
0.218 |
3.723 |
4.579 |
Plots for ancova
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "zona.escola", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:zona.escola"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["zona.escola"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggPlotAoC2(pwcs, "zona.escola", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:zona.escola"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.vocab.ensinado.pos", c("grupo","zona.escola"), aov, pwcs, covar = "score.vocab.ensinado.pre",
theme = "classic", color = color[["grupo:zona.escola"]],
subtitle = which(aov$Effect == "grupo:zona.escola"))
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots[["grupo:zona.escola"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.vocab.ensinado", c("grupo","zona.escola"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["zona.escola"]])) >= 2)
plots[["grupo:zona.escola"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
facet.by = c("grupo","zona.escola"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
color = "grupo", facet.by = "zona.escola", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.escola"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
color = "zona.escola", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = zona.escola)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.escola"))) +
ggplot2::scale_color_manual(values = color[["zona.escola"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["zona.escola"]])) >= 2)
res <- augment(lm(score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*zona.escola, data = wdat))
if (length(unique(pdat[["zona.escola"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.997 0.796
if (length(unique(pdat[["zona.escola"]])) >= 2)
levene_test(res, .resid ~ grupo*zona.escola)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 307 1.60 0.190
ANCOVA
and Pairwise for two factors
grupo:score.vocab.ensinado.quintile
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["score.vocab.ensinado.quintile"]]),],
"score.vocab.ensinado.pos", c("grupo","score.vocab.ensinado.quintile"))
pdat = pdat[pdat[["score.vocab.ensinado.quintile"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["score.vocab.ensinado.quintile"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["score.vocab.ensinado.quintile"]] = factor(
pdat[["score.vocab.ensinado.quintile"]],
level[["score.vocab.ensinado.quintile"]][level[["score.vocab.ensinado.quintile"]] %in% unique(pdat[["score.vocab.ensinado.quintile"]])])
pdat.long <- rbind(pdat[,c("id","grupo","score.vocab.ensinado.quintile")], pdat[,c("id","grupo","score.vocab.ensinado.quintile")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.vocab.ensinado"]] <- c(pdat[["score.vocab.ensinado.pre"]], pdat[["score.vocab.ensinado.pos"]])
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
aov = anova_test(pdat, score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*score.vocab.ensinado.quintile)
laov[["grupo:score.vocab.ensinado.quintile"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["score.vocab.ensinado.quintile"]] <- emmeans_test(
group_by(pdat, grupo), score.vocab.ensinado.pos ~ score.vocab.ensinado.quintile,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, score.vocab.ensinado.quintile), score.vocab.ensinado.pos ~ grupo,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["score.vocab.ensinado.quintile"]])
pwc <- pwc[,c("grupo","score.vocab.ensinado.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","score.vocab.ensinado.quintile")])]
}
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","score.vocab.ensinado.quintile")),
score.vocab.ensinado ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:score.vocab.ensinado.quintile"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
ds <- get.descriptives(pdat, "score.vocab.ensinado.pos", c("grupo","score.vocab.ensinado.quintile"), covar = "score.vocab.ensinado.pre")
ds <- merge(ds[ds$variable != "score.vocab.ensinado.pre",],
ds[ds$variable == "score.vocab.ensinado.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","score.vocab.ensinado.quintile"), all.x = T, suffixes = c("", ".score.vocab.ensinado.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","score.vocab.ensinado.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","score.vocab.ensinado.quintile","n","mean.score.vocab.ensinado.pre","se.score.vocab.ensinado.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","score.vocab.ensinado.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:score.vocab.ensinado.quintile"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*score.vocab.ensinado.quintile, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","score.vocab.ensinado.quintile")], wdat[,c("id","grupo","score.vocab.ensinado.quintile")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.vocab.ensinado"]] <- c(wdat[["score.vocab.ensinado.pre"]], wdat[["score.vocab.ensinado.pos"]])
ldat[["grupo:score.vocab.ensinado.quintile"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
aov = anova_test(wdat, score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*score.vocab.ensinado.quintile)
laov[["grupo:score.vocab.ensinado.quintile"]] <- merge(get_anova_table(aov), laov[["grupo:score.vocab.ensinado.quintile"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.vocab.ensinado.pre |
1 |
292 |
8.490 |
0.004 |
* |
0.028 |
| grupo |
1 |
292 |
1.851 |
0.175 |
|
0.006 |
| score.vocab.ensinado.quintile |
3 |
292 |
2.725 |
0.044 |
* |
0.027 |
| grupo:score.vocab.ensinado.quintile |
3 |
292 |
1.497 |
0.215 |
|
0.015 |
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["score.vocab.ensinado.quintile"]] <- emmeans_test(
group_by(wdat, grupo), score.vocab.ensinado.pos ~ score.vocab.ensinado.quintile,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, score.vocab.ensinado.quintile), score.vocab.ensinado.pos ~ grupo,
covariate = score.vocab.ensinado.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["score.vocab.ensinado.quintile"]])
pwc <- pwc[,c("grupo","score.vocab.ensinado.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","score.vocab.ensinado.quintile")])]
}
|
1st quintile |
score.vocab.ensinado.pre*grupo |
score.vocab.ensinado.pos |
Controle |
Experimental |
292 |
-0.950 |
0.343 |
0.343 |
ns |
|
2nd quintile |
score.vocab.ensinado.pre*grupo |
score.vocab.ensinado.pos |
Controle |
Experimental |
292 |
-2.309 |
0.022 |
0.022 |
* |
|
3rd quintile |
score.vocab.ensinado.pre*grupo |
score.vocab.ensinado.pos |
Controle |
Experimental |
292 |
0.273 |
0.785 |
0.785 |
ns |
|
4th quintile |
score.vocab.ensinado.pre*grupo |
score.vocab.ensinado.pos |
Controle |
Experimental |
292 |
0.165 |
0.869 |
0.869 |
ns |
| Controle |
|
score.vocab.ensinado.pre*score.vocab.ensinado.quintile |
score.vocab.ensinado.pos |
1st quintile |
2nd quintile |
292 |
1.589 |
0.113 |
0.678 |
ns |
| Controle |
|
score.vocab.ensinado.pre*score.vocab.ensinado.quintile |
score.vocab.ensinado.pos |
1st quintile |
3rd quintile |
292 |
1.159 |
0.247 |
1.000 |
ns |
| Controle |
|
score.vocab.ensinado.pre*score.vocab.ensinado.quintile |
score.vocab.ensinado.pos |
1st quintile |
4th quintile |
292 |
0.359 |
0.720 |
1.000 |
ns |
| Controle |
|
score.vocab.ensinado.pre*score.vocab.ensinado.quintile |
score.vocab.ensinado.pos |
2nd quintile |
3rd quintile |
292 |
0.164 |
0.870 |
1.000 |
ns |
| Controle |
|
score.vocab.ensinado.pre*score.vocab.ensinado.quintile |
score.vocab.ensinado.pos |
2nd quintile |
4th quintile |
292 |
-0.410 |
0.682 |
1.000 |
ns |
| Controle |
|
score.vocab.ensinado.pre*score.vocab.ensinado.quintile |
score.vocab.ensinado.pos |
3rd quintile |
4th quintile |
292 |
-0.713 |
0.477 |
1.000 |
ns |
| Experimental |
|
score.vocab.ensinado.pre*score.vocab.ensinado.quintile |
score.vocab.ensinado.pos |
1st quintile |
2nd quintile |
292 |
0.753 |
0.452 |
1.000 |
ns |
| Experimental |
|
score.vocab.ensinado.pre*score.vocab.ensinado.quintile |
score.vocab.ensinado.pos |
1st quintile |
3rd quintile |
292 |
1.810 |
0.071 |
0.428 |
ns |
| Experimental |
|
score.vocab.ensinado.pre*score.vocab.ensinado.quintile |
score.vocab.ensinado.pos |
1st quintile |
4th quintile |
292 |
0.851 |
0.395 |
1.000 |
ns |
| Experimental |
|
score.vocab.ensinado.pre*score.vocab.ensinado.quintile |
score.vocab.ensinado.pos |
2nd quintile |
3rd quintile |
292 |
2.008 |
0.046 |
0.274 |
ns |
| Experimental |
|
score.vocab.ensinado.pre*score.vocab.ensinado.quintile |
score.vocab.ensinado.pos |
2nd quintile |
4th quintile |
292 |
0.635 |
0.526 |
1.000 |
ns |
| Experimental |
|
score.vocab.ensinado.pre*score.vocab.ensinado.quintile |
score.vocab.ensinado.pos |
3rd quintile |
4th quintile |
292 |
-0.656 |
0.512 |
1.000 |
ns |
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","score.vocab.ensinado.quintile")),
score.vocab.ensinado ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:score.vocab.ensinado.quintile"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:score.vocab.ensinado.quintile"]],
by=c("grupo","score.vocab.ensinado.quintile","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
1st quintile |
time |
score.vocab.ensinado |
pre |
pos |
586 |
-5.629 |
0.000 |
0.000 |
**** |
| Controle |
2nd quintile |
time |
score.vocab.ensinado |
pre |
pos |
586 |
-1.078 |
0.281 |
0.281 |
ns |
| Controle |
3rd quintile |
time |
score.vocab.ensinado |
pre |
pos |
586 |
2.714 |
0.007 |
0.007 |
** |
| Controle |
4th quintile |
time |
score.vocab.ensinado |
pre |
pos |
586 |
2.502 |
0.013 |
0.013 |
* |
| Experimental |
1st quintile |
time |
score.vocab.ensinado |
pre |
pos |
586 |
-4.864 |
0.000 |
0.000 |
**** |
| Experimental |
2nd quintile |
time |
score.vocab.ensinado |
pre |
pos |
586 |
-3.981 |
0.000 |
0.000 |
**** |
| Experimental |
3rd quintile |
time |
score.vocab.ensinado |
pre |
pos |
586 |
2.948 |
0.003 |
0.003 |
** |
| Experimental |
4th quintile |
time |
score.vocab.ensinado |
pre |
pos |
586 |
2.397 |
0.017 |
0.017 |
* |
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
ds <- get.descriptives(wdat, "score.vocab.ensinado.pos", c("grupo","score.vocab.ensinado.quintile"), covar = "score.vocab.ensinado.pre")
ds <- merge(ds[ds$variable != "score.vocab.ensinado.pre",],
ds[ds$variable == "score.vocab.ensinado.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","score.vocab.ensinado.quintile"), all.x = T, suffixes = c("", ".score.vocab.ensinado.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","score.vocab.ensinado.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","score.vocab.ensinado.quintile","n","mean.score.vocab.ensinado.pre","se.score.vocab.ensinado.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","score.vocab.ensinado.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:score.vocab.ensinado.quintile"]] <- merge(ds, lemms[["grupo:score.vocab.ensinado.quintile"]],
by=c("grupo","score.vocab.ensinado.quintile"), suffixes = c("","'"))
}
| Controle |
1st quintile |
32 |
0.562 |
0.089 |
2.781 |
0.341 |
4.566 |
0.717 |
3.155 |
5.978 |
| Controle |
2nd quintile |
50 |
2.600 |
0.070 |
2.940 |
0.292 |
3.586 |
0.371 |
2.854 |
4.317 |
| Controle |
3rd quintile |
71 |
4.789 |
0.094 |
4.070 |
0.240 |
3.492 |
0.319 |
2.863 |
4.121 |
| Controle |
4th quintile |
17 |
7.471 |
0.125 |
6.118 |
0.542 |
4.039 |
0.878 |
2.311 |
5.766 |
| Experimental |
1st quintile |
15 |
0.667 |
0.126 |
3.467 |
0.533 |
5.194 |
0.805 |
3.610 |
6.778 |
| Experimental |
2nd quintile |
37 |
2.459 |
0.083 |
3.919 |
0.341 |
4.643 |
0.427 |
3.804 |
5.483 |
| Experimental |
3rd quintile |
65 |
4.785 |
0.094 |
3.969 |
0.284 |
3.393 |
0.328 |
2.747 |
4.038 |
| Experimental |
4th quintile |
14 |
7.357 |
0.133 |
5.929 |
0.722 |
3.913 |
0.892 |
2.157 |
5.669 |
Plots for ancova
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "score.vocab.ensinado.quintile", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:score.vocab.ensinado.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["score.vocab.ensinado.quintile"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "score.vocab.ensinado.quintile", "grupo", aov, ylab = "Vocabulary taught",
subtitle = which(aov$Effect == "grupo:score.vocab.ensinado.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.vocab.ensinado.pos", c("grupo","score.vocab.ensinado.quintile"), aov, pwcs, covar = "score.vocab.ensinado.pre",
theme = "classic", color = color[["grupo:score.vocab.ensinado.quintile"]],
subtitle = which(aov$Effect == "grupo:score.vocab.ensinado.quintile"))
}
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
plots[["grupo:score.vocab.ensinado.quintile"]] + ggplot2::ylab("Vocabulary taught") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.vocab.ensinado", c("grupo","score.vocab.ensinado.quintile"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2)
plots[["grupo:score.vocab.ensinado.quintile"]] + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
facet.by = c("grupo","score.vocab.ensinado.quintile"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
color = "grupo", facet.by = "score.vocab.ensinado.quintile", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:score.vocab.ensinado.quintile"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.vocab.ensinado.pre", y = "score.vocab.ensinado.pos", size = 0.5,
color = "score.vocab.ensinado.quintile", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = score.vocab.ensinado.quintile)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:score.vocab.ensinado.quintile"))) +
ggplot2::scale_color_manual(values = color[["score.vocab.ensinado.quintile"]]) +
ggplot2::ylab("Vocabulary taught") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2)
res <- augment(lm(score.vocab.ensinado.pos ~ score.vocab.ensinado.pre + grupo*score.vocab.ensinado.quintile, data = wdat))
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.995 0.415
if (length(unique(pdat[["score.vocab.ensinado.quintile"]])) >= 2)
levene_test(res, .resid ~ grupo*score.vocab.ensinado.quintile)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 293 0.438 0.878
Summary of Results
Descriptive Statistics
df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
score.vocab.ensinado.pre |
176 |
3.807 |
4.0 |
0 |
10 |
2.327 |
0.175 |
0.346 |
3.00 |
YES |
0.328 |
-0.438 |
| Experimental |
|
|
|
|
score.vocab.ensinado.pre |
135 |
4.104 |
4.0 |
0 |
10 |
2.180 |
0.188 |
0.371 |
3.00 |
YES |
0.358 |
-0.067 |
|
|
|
|
|
score.vocab.ensinado.pre |
311 |
3.936 |
4.0 |
0 |
10 |
2.265 |
0.128 |
0.253 |
3.00 |
YES |
0.327 |
-0.275 |
| Controle |
|
|
|
|
score.vocab.ensinado.pos |
176 |
3.795 |
4.0 |
0 |
10 |
2.276 |
0.172 |
0.339 |
4.00 |
YES |
0.228 |
-0.735 |
| Experimental |
|
|
|
|
score.vocab.ensinado.pos |
135 |
4.215 |
4.0 |
0 |
10 |
2.389 |
0.206 |
0.407 |
3.00 |
YES |
0.047 |
-0.472 |
|
|
|
|
|
score.vocab.ensinado.pos |
311 |
3.977 |
4.0 |
0 |
10 |
2.331 |
0.132 |
0.260 |
4.00 |
YES |
0.155 |
-0.606 |
| Controle |
F |
|
|
|
score.vocab.ensinado.pre |
87 |
3.954 |
4.0 |
0 |
10 |
2.401 |
0.257 |
0.512 |
3.00 |
YES |
0.437 |
-0.336 |
| Controle |
M |
|
|
|
score.vocab.ensinado.pre |
89 |
3.663 |
4.0 |
0 |
9 |
2.256 |
0.239 |
0.475 |
3.00 |
YES |
0.171 |
-0.750 |
| Experimental |
F |
|
|
|
score.vocab.ensinado.pre |
59 |
4.576 |
4.0 |
0 |
10 |
2.313 |
0.301 |
0.603 |
3.00 |
YES |
0.479 |
-0.187 |
| Experimental |
M |
|
|
|
score.vocab.ensinado.pre |
75 |
3.680 |
4.0 |
0 |
8 |
1.960 |
0.226 |
0.451 |
3.00 |
YES |
0.020 |
-0.671 |
| Controle |
F |
|
|
|
score.vocab.ensinado.pos |
87 |
4.057 |
4.0 |
0 |
10 |
2.364 |
0.253 |
0.504 |
4.00 |
YES |
0.224 |
-0.888 |
| Controle |
M |
|
|
|
score.vocab.ensinado.pos |
89 |
3.539 |
3.0 |
0 |
9 |
2.169 |
0.230 |
0.457 |
3.00 |
YES |
0.172 |
-0.724 |
| Experimental |
F |
|
|
|
score.vocab.ensinado.pos |
59 |
4.542 |
5.0 |
0 |
10 |
2.514 |
0.327 |
0.655 |
3.00 |
YES |
-0.139 |
-0.643 |
| Experimental |
M |
|
|
|
score.vocab.ensinado.pos |
75 |
4.013 |
4.0 |
0 |
10 |
2.239 |
0.259 |
0.515 |
2.00 |
YES |
0.183 |
-0.236 |
| Controle |
|
Rural |
|
|
score.vocab.ensinado.pre |
56 |
3.696 |
3.0 |
0 |
10 |
2.536 |
0.339 |
0.679 |
3.00 |
NO |
0.598 |
-0.231 |
| Controle |
|
Urbana |
|
|
score.vocab.ensinado.pre |
77 |
3.896 |
4.0 |
0 |
9 |
2.303 |
0.262 |
0.523 |
3.00 |
YES |
0.074 |
-0.796 |
| Experimental |
|
Rural |
|
|
score.vocab.ensinado.pre |
52 |
3.808 |
4.0 |
0 |
10 |
2.232 |
0.310 |
0.621 |
3.00 |
NO |
0.517 |
-0.054 |
| Experimental |
|
Urbana |
|
|
score.vocab.ensinado.pre |
49 |
4.163 |
4.0 |
0 |
10 |
2.115 |
0.302 |
0.607 |
3.00 |
YES |
0.269 |
-0.192 |
| Controle |
|
Rural |
|
|
score.vocab.ensinado.pos |
56 |
4.161 |
4.0 |
1 |
8 |
1.980 |
0.265 |
0.530 |
3.00 |
YES |
0.444 |
-0.978 |
| Controle |
|
Urbana |
|
|
score.vocab.ensinado.pos |
77 |
3.935 |
4.0 |
0 |
10 |
2.478 |
0.282 |
0.562 |
4.00 |
YES |
0.171 |
-0.821 |
| Experimental |
|
Rural |
|
|
score.vocab.ensinado.pos |
52 |
4.712 |
4.0 |
1 |
9 |
1.934 |
0.268 |
0.538 |
3.00 |
YES |
0.370 |
-0.430 |
| Experimental |
|
Urbana |
|
|
score.vocab.ensinado.pos |
49 |
4.286 |
4.0 |
0 |
10 |
2.458 |
0.351 |
0.706 |
3.00 |
YES |
-0.126 |
-0.737 |
| Controle |
|
|
Rural |
|
score.vocab.ensinado.pre |
58 |
3.897 |
4.0 |
0 |
9 |
2.330 |
0.306 |
0.613 |
2.75 |
YES |
0.246 |
-0.616 |
| Controle |
|
|
Urbana |
|
score.vocab.ensinado.pre |
118 |
3.763 |
4.0 |
0 |
10 |
2.334 |
0.215 |
0.426 |
3.00 |
YES |
0.365 |
-0.385 |
| Experimental |
|
|
Rural |
|
score.vocab.ensinado.pre |
40 |
4.350 |
4.5 |
0 |
10 |
2.497 |
0.395 |
0.798 |
3.25 |
YES |
0.487 |
-0.441 |
| Experimental |
|
|
Urbana |
|
score.vocab.ensinado.pre |
95 |
4.000 |
4.0 |
0 |
10 |
2.037 |
0.209 |
0.415 |
2.00 |
YES |
0.172 |
-0.186 |
| Controle |
|
|
Rural |
|
score.vocab.ensinado.pos |
58 |
4.052 |
3.5 |
0 |
8 |
2.164 |
0.284 |
0.569 |
4.00 |
YES |
0.323 |
-1.087 |
| Controle |
|
|
Urbana |
|
score.vocab.ensinado.pos |
118 |
3.669 |
4.0 |
0 |
10 |
2.328 |
0.214 |
0.424 |
3.00 |
YES |
0.214 |
-0.673 |
| Experimental |
|
|
Rural |
|
score.vocab.ensinado.pos |
40 |
4.300 |
4.0 |
0 |
10 |
2.267 |
0.358 |
0.725 |
2.00 |
YES |
0.332 |
-0.063 |
| Experimental |
|
|
Urbana |
|
score.vocab.ensinado.pos |
95 |
4.179 |
4.0 |
0 |
10 |
2.449 |
0.251 |
0.499 |
3.00 |
YES |
-0.042 |
-0.678 |
| Controle |
|
|
|
1st quintile |
score.vocab.ensinado.pre |
32 |
0.562 |
1.0 |
0 |
1 |
0.504 |
0.089 |
0.182 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
2nd quintile |
score.vocab.ensinado.pre |
50 |
2.600 |
3.0 |
2 |
3 |
0.495 |
0.070 |
0.141 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
3rd quintile |
score.vocab.ensinado.pre |
71 |
4.789 |
5.0 |
4 |
6 |
0.791 |
0.094 |
0.187 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
4th quintile |
score.vocab.ensinado.pre |
17 |
7.471 |
7.0 |
7 |
8 |
0.514 |
0.125 |
0.265 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
1st quintile |
score.vocab.ensinado.pre |
15 |
0.667 |
1.0 |
0 |
1 |
0.488 |
0.126 |
0.270 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
2nd quintile |
score.vocab.ensinado.pre |
37 |
2.459 |
2.0 |
2 |
3 |
0.505 |
0.083 |
0.168 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
3rd quintile |
score.vocab.ensinado.pre |
65 |
4.785 |
5.0 |
4 |
6 |
0.760 |
0.094 |
0.188 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
4th quintile |
score.vocab.ensinado.pre |
14 |
7.357 |
7.0 |
7 |
8 |
0.497 |
0.133 |
0.287 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
1st quintile |
score.vocab.ensinado.pos |
32 |
2.781 |
2.0 |
0 |
8 |
1.930 |
0.341 |
0.696 |
2.00 |
NO |
0.692 |
0.070 |
| Controle |
|
|
|
2nd quintile |
score.vocab.ensinado.pos |
50 |
2.940 |
3.0 |
0 |
8 |
2.064 |
0.292 |
0.587 |
2.75 |
NO |
0.733 |
-0.284 |
| Controle |
|
|
|
3rd quintile |
score.vocab.ensinado.pos |
71 |
4.070 |
4.0 |
0 |
9 |
2.024 |
0.240 |
0.479 |
2.00 |
YES |
-0.074 |
-0.377 |
| Controle |
|
|
|
4th quintile |
score.vocab.ensinado.pos |
17 |
6.118 |
6.0 |
0 |
10 |
2.233 |
0.542 |
1.148 |
2.00 |
NO |
-0.963 |
1.182 |
| Experimental |
|
|
|
1st quintile |
score.vocab.ensinado.pos |
15 |
3.467 |
4.0 |
0 |
7 |
2.066 |
0.533 |
1.144 |
3.00 |
YES |
-0.047 |
-1.346 |
| Experimental |
|
|
|
2nd quintile |
score.vocab.ensinado.pos |
37 |
3.919 |
4.0 |
0 |
8 |
2.073 |
0.341 |
0.691 |
2.00 |
YES |
-0.150 |
-0.313 |
| Experimental |
|
|
|
3rd quintile |
score.vocab.ensinado.pos |
65 |
3.969 |
4.0 |
0 |
9 |
2.291 |
0.284 |
0.568 |
4.00 |
YES |
-0.047 |
-0.719 |
| Experimental |
|
|
|
4th quintile |
score.vocab.ensinado.pos |
14 |
5.929 |
6.0 |
0 |
10 |
2.702 |
0.722 |
1.560 |
3.25 |
NO |
-0.603 |
-0.449 |
ANCOVA Table Comparison
df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
| 1 |
grupo |
1 |
308 |
1.447 |
0.230 |
|
0.005 |
1 |
308 |
1.447 |
0.230 |
|
0.005 |
| 2 |
score.vocab.ensinado.pre |
1 |
308 |
65.187 |
0.000 |
* |
0.175 |
1 |
308 |
65.187 |
0.000 |
* |
0.175 |
| 3 |
genero |
1 |
305 |
1.382 |
0.241 |
|
0.005 |
1 |
305 |
1.382 |
0.241 |
|
0.005 |
| 5 |
grupo:genero |
1 |
305 |
0.277 |
0.599 |
|
0.001 |
1 |
305 |
0.277 |
0.599 |
|
0.001 |
| 8 |
grupo:zona.participante |
1 |
229 |
0.252 |
0.616 |
|
0.001 |
1 |
229 |
0.252 |
0.616 |
|
0.001 |
| 10 |
zona.participante |
1 |
229 |
2.623 |
0.107 |
|
0.011 |
1 |
229 |
2.623 |
0.107 |
|
0.011 |
| 12 |
grupo:zona.escola |
1 |
306 |
0.455 |
0.501 |
|
0.001 |
1 |
306 |
0.455 |
0.501 |
|
0.001 |
| 14 |
zona.escola |
1 |
306 |
0.464 |
0.496 |
|
0.002 |
1 |
306 |
0.464 |
0.496 |
|
0.002 |
| 16 |
grupo:score.vocab.ensinado.quintile |
3 |
292 |
1.497 |
0.215 |
|
0.015 |
3 |
292 |
1.497 |
0.215 |
|
0.015 |
| 18 |
score.vocab.ensinado.quintile |
3 |
292 |
2.725 |
0.044 |
* |
0.027 |
3 |
292 |
2.725 |
0.044 |
* |
0.027 |
PairWise Table Comparison
df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
| Controle |
|
|
|
|
pre |
pos |
618 |
0.046 |
0.963 |
0.963 |
ns |
618 |
0.046 |
0.963 |
0.963 |
ns |
| Experimental |
|
|
|
|
pre |
pos |
618 |
-0.398 |
0.691 |
0.691 |
ns |
618 |
-0.398 |
0.691 |
0.691 |
ns |
|
|
|
|
|
Controle |
Experimental |
308 |
-1.203 |
0.230 |
0.230 |
ns |
308 |
-1.203 |
0.230 |
0.230 |
ns |
| Controle |
F |
|
|
|
pre |
pos |
612 |
-0.300 |
0.764 |
0.764 |
ns |
612 |
-0.300 |
0.764 |
0.764 |
ns |
| Controle |
M |
|
|
|
pre |
pos |
612 |
0.362 |
0.717 |
0.717 |
ns |
612 |
0.362 |
0.717 |
0.717 |
ns |
| Controle |
|
|
|
|
F |
M |
305 |
1.236 |
0.217 |
0.217 |
ns |
305 |
1.236 |
0.217 |
0.217 |
ns |
| Experimental |
F |
|
|
|
pre |
pos |
612 |
0.081 |
0.936 |
0.936 |
ns |
612 |
0.081 |
0.936 |
0.936 |
ns |
| Experimental |
M |
|
|
|
pre |
pos |
612 |
-0.897 |
0.370 |
0.370 |
ns |
612 |
-0.897 |
0.370 |
0.370 |
ns |
| Experimental |
|
|
|
|
F |
M |
305 |
0.371 |
0.711 |
0.711 |
ns |
305 |
0.371 |
0.711 |
0.711 |
ns |
|
F |
|
|
|
Controle |
Experimental |
305 |
-0.598 |
0.550 |
0.550 |
ns |
305 |
-0.598 |
0.550 |
0.550 |
ns |
|
M |
|
|
|
Controle |
Experimental |
305 |
-1.422 |
0.156 |
0.156 |
ns |
305 |
-1.422 |
0.156 |
0.156 |
ns |
| Controle |
|
|
|
|
Rural |
Urbana |
229 |
0.885 |
0.377 |
0.377 |
ns |
229 |
0.885 |
0.377 |
0.377 |
ns |
| Controle |
|
Rural |
|
|
pre |
pos |
460 |
-1.078 |
0.282 |
0.282 |
ns |
460 |
-1.078 |
0.282 |
0.282 |
ns |
| Controle |
|
Urbana |
|
|
pre |
pos |
460 |
-0.106 |
0.916 |
0.916 |
ns |
460 |
-0.106 |
0.916 |
0.916 |
ns |
| Experimental |
|
|
|
|
Rural |
Urbana |
229 |
1.448 |
0.149 |
0.149 |
ns |
229 |
1.448 |
0.149 |
0.149 |
ns |
| Experimental |
|
Rural |
|
|
pre |
pos |
460 |
-2.022 |
0.044 |
0.044 |
* |
460 |
-2.022 |
0.044 |
0.044 |
* |
| Experimental |
|
Urbana |
|
|
pre |
pos |
460 |
-0.266 |
0.790 |
0.790 |
ns |
460 |
-0.266 |
0.790 |
0.790 |
ns |
|
|
Rural |
|
|
Controle |
Experimental |
229 |
-1.295 |
0.197 |
0.197 |
ns |
229 |
-1.295 |
0.197 |
0.197 |
ns |
|
|
Urbana |
|
|
Controle |
Experimental |
229 |
-0.635 |
0.526 |
0.526 |
ns |
229 |
-0.635 |
0.526 |
0.526 |
ns |
| Controle |
|
|
|
|
Rural |
Urbana |
306 |
0.955 |
0.340 |
0.340 |
ns |
306 |
0.955 |
0.340 |
0.340 |
ns |
| Controle |
|
|
Rural |
|
pre |
pos |
614 |
-0.363 |
0.716 |
0.716 |
ns |
614 |
-0.363 |
0.716 |
0.716 |
ns |
| Controle |
|
|
Urbana |
|
pre |
pos |
614 |
0.311 |
0.756 |
0.756 |
ns |
614 |
0.311 |
0.756 |
0.756 |
ns |
| Experimental |
|
|
|
|
Rural |
Urbana |
306 |
-0.072 |
0.942 |
0.942 |
ns |
306 |
-0.072 |
0.942 |
0.942 |
ns |
| Experimental |
|
|
Rural |
|
pre |
pos |
614 |
0.097 |
0.923 |
0.923 |
ns |
614 |
0.097 |
0.923 |
0.923 |
ns |
| Experimental |
|
|
Urbana |
|
pre |
pos |
614 |
-0.536 |
0.592 |
0.592 |
ns |
614 |
-0.536 |
0.592 |
0.592 |
ns |
|
|
|
Rural |
|
Controle |
Experimental |
306 |
-0.124 |
0.902 |
0.902 |
ns |
306 |
-0.124 |
0.902 |
0.902 |
ns |
|
|
|
Urbana |
|
Controle |
Experimental |
306 |
-1.394 |
0.164 |
0.164 |
ns |
306 |
-1.394 |
0.164 |
0.164 |
ns |
| Controle |
|
|
|
1st quintile |
pre |
pos |
586 |
-5.629 |
0.000 |
0.000 |
**** |
586 |
-5.629 |
0.000 |
0.000 |
**** |
| Controle |
|
|
|
2nd quintile |
pre |
pos |
586 |
-1.078 |
0.281 |
0.281 |
ns |
586 |
-1.078 |
0.281 |
0.281 |
ns |
| Controle |
|
|
|
3rd quintile |
pre |
pos |
586 |
2.714 |
0.007 |
0.007 |
** |
586 |
2.714 |
0.007 |
0.007 |
** |
| Controle |
|
|
|
4th quintile |
pre |
pos |
586 |
2.502 |
0.013 |
0.013 |
* |
586 |
2.502 |
0.013 |
0.013 |
* |
| Controle |
|
|
|
|
1st quintile |
2nd quintile |
292 |
1.589 |
0.113 |
0.678 |
ns |
292 |
1.589 |
0.113 |
0.678 |
ns |
| Controle |
|
|
|
|
1st quintile |
3rd quintile |
292 |
1.159 |
0.247 |
1.000 |
ns |
292 |
1.159 |
0.247 |
1.000 |
ns |
| Controle |
|
|
|
|
1st quintile |
4th quintile |
292 |
0.359 |
0.720 |
1.000 |
ns |
292 |
0.359 |
0.720 |
1.000 |
ns |
| Controle |
|
|
|
|
2nd quintile |
3rd quintile |
292 |
0.164 |
0.870 |
1.000 |
ns |
292 |
0.164 |
0.870 |
1.000 |
ns |
| Controle |
|
|
|
|
2nd quintile |
4th quintile |
292 |
-0.410 |
0.682 |
1.000 |
ns |
292 |
-0.410 |
0.682 |
1.000 |
ns |
| Controle |
|
|
|
|
3rd quintile |
4th quintile |
292 |
-0.713 |
0.477 |
1.000 |
ns |
292 |
-0.713 |
0.477 |
1.000 |
ns |
| Experimental |
|
|
|
1st quintile |
pre |
pos |
586 |
-4.864 |
0.000 |
0.000 |
**** |
586 |
-4.864 |
0.000 |
0.000 |
**** |
| Experimental |
|
|
|
2nd quintile |
pre |
pos |
586 |
-3.981 |
0.000 |
0.000 |
**** |
586 |
-3.981 |
0.000 |
0.000 |
**** |
| Experimental |
|
|
|
3rd quintile |
pre |
pos |
586 |
2.948 |
0.003 |
0.003 |
** |
586 |
2.948 |
0.003 |
0.003 |
** |
| Experimental |
|
|
|
4th quintile |
pre |
pos |
586 |
2.397 |
0.017 |
0.017 |
* |
586 |
2.397 |
0.017 |
0.017 |
* |
| Experimental |
|
|
|
|
1st quintile |
2nd quintile |
292 |
0.753 |
0.452 |
1.000 |
ns |
292 |
0.753 |
0.452 |
1.000 |
ns |
| Experimental |
|
|
|
|
1st quintile |
3rd quintile |
292 |
1.810 |
0.071 |
0.428 |
ns |
292 |
1.810 |
0.071 |
0.428 |
ns |
| Experimental |
|
|
|
|
1st quintile |
4th quintile |
292 |
0.851 |
0.395 |
1.000 |
ns |
292 |
0.851 |
0.395 |
1.000 |
ns |
| Experimental |
|
|
|
|
2nd quintile |
3rd quintile |
292 |
2.008 |
0.046 |
0.274 |
ns |
292 |
2.008 |
0.046 |
0.274 |
ns |
| Experimental |
|
|
|
|
2nd quintile |
4th quintile |
292 |
0.635 |
0.526 |
1.000 |
ns |
292 |
0.635 |
0.526 |
1.000 |
ns |
| Experimental |
|
|
|
|
3rd quintile |
4th quintile |
292 |
-0.656 |
0.512 |
1.000 |
ns |
292 |
-0.656 |
0.512 |
1.000 |
ns |
|
|
|
|
1st quintile |
Controle |
Experimental |
292 |
-0.950 |
0.343 |
0.343 |
ns |
292 |
-0.950 |
0.343 |
0.343 |
ns |
|
|
|
|
2nd quintile |
Controle |
Experimental |
292 |
-2.309 |
0.022 |
0.022 |
* |
292 |
-2.309 |
0.022 |
0.022 |
* |
|
|
|
|
3rd quintile |
Controle |
Experimental |
292 |
0.273 |
0.785 |
0.785 |
ns |
292 |
0.273 |
0.785 |
0.785 |
ns |
|
|
|
|
4th quintile |
Controle |
Experimental |
292 |
0.165 |
0.869 |
0.869 |
ns |
292 |
0.165 |
0.869 |
0.869 |
ns |
EMMS Table Comparison
df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% names(lfatores)])]
| Controle |
|
|
|
|
176 |
3.807 |
0.175 |
3.795 |
0.172 |
3.851 |
0.160 |
3.537 |
4.165 |
176 |
3.807 |
0.175 |
3.795 |
0.172 |
3.851 |
0.160 |
3.537 |
4.165 |
0 |
| Experimental |
|
|
|
|
135 |
4.104 |
0.188 |
4.215 |
0.206 |
4.143 |
0.182 |
3.784 |
4.502 |
135 |
4.104 |
0.188 |
4.215 |
0.206 |
4.143 |
0.182 |
3.784 |
4.502 |
0 |
| Controle |
F |
|
|
|
87 |
3.954 |
0.257 |
4.057 |
0.253 |
4.044 |
0.224 |
3.602 |
4.485 |
87 |
3.954 |
0.257 |
4.057 |
0.253 |
4.044 |
0.224 |
3.602 |
4.485 |
0 |
| Controle |
M |
|
|
|
89 |
3.663 |
0.239 |
3.539 |
0.230 |
3.653 |
0.222 |
3.216 |
4.091 |
89 |
3.663 |
0.239 |
3.539 |
0.230 |
3.653 |
0.222 |
3.216 |
4.091 |
0 |
| Experimental |
F |
|
|
|
59 |
4.576 |
0.301 |
4.542 |
0.327 |
4.256 |
0.275 |
3.715 |
4.797 |
59 |
4.576 |
0.301 |
4.542 |
0.327 |
4.256 |
0.275 |
3.715 |
4.797 |
0 |
| Experimental |
M |
|
|
|
75 |
3.680 |
0.226 |
4.013 |
0.259 |
4.120 |
0.242 |
3.643 |
4.596 |
75 |
3.680 |
0.226 |
4.013 |
0.259 |
4.120 |
0.242 |
3.643 |
4.596 |
0 |
| Controle |
|
Rural |
|
|
56 |
3.696 |
0.339 |
4.161 |
0.265 |
4.243 |
0.269 |
3.712 |
4.774 |
56 |
3.696 |
0.339 |
4.161 |
0.265 |
4.243 |
0.269 |
3.712 |
4.774 |
0 |
| Controle |
|
Urbana |
|
|
77 |
3.896 |
0.262 |
3.935 |
0.282 |
3.930 |
0.229 |
3.478 |
4.382 |
77 |
3.896 |
0.262 |
3.935 |
0.282 |
3.930 |
0.229 |
3.478 |
4.382 |
0 |
| Experimental |
|
Rural |
|
|
52 |
3.808 |
0.310 |
4.712 |
0.268 |
4.745 |
0.279 |
4.195 |
5.295 |
52 |
3.808 |
0.310 |
4.712 |
0.268 |
4.745 |
0.279 |
4.195 |
5.295 |
0 |
| Experimental |
|
Urbana |
|
|
49 |
4.163 |
0.302 |
4.286 |
0.351 |
4.164 |
0.288 |
3.596 |
4.732 |
49 |
4.163 |
0.302 |
4.286 |
0.351 |
4.164 |
0.288 |
3.596 |
4.732 |
0 |
| Controle |
|
|
Rural |
|
58 |
3.897 |
0.306 |
4.052 |
0.284 |
4.068 |
0.278 |
3.521 |
4.616 |
58 |
3.897 |
0.306 |
4.052 |
0.284 |
4.068 |
0.278 |
3.521 |
4.616 |
0 |
| Controle |
|
|
Urbana |
|
118 |
3.763 |
0.215 |
3.669 |
0.214 |
3.744 |
0.195 |
3.359 |
4.128 |
118 |
3.763 |
0.215 |
3.669 |
0.214 |
3.744 |
0.195 |
3.359 |
4.128 |
0 |
| Experimental |
|
|
Rural |
|
40 |
4.350 |
0.395 |
4.300 |
0.358 |
4.122 |
0.336 |
3.461 |
4.784 |
40 |
4.350 |
0.395 |
4.300 |
0.358 |
4.122 |
0.336 |
3.461 |
4.784 |
0 |
| Experimental |
|
|
Urbana |
|
95 |
4.000 |
0.209 |
4.179 |
0.251 |
4.151 |
0.218 |
3.723 |
4.579 |
95 |
4.000 |
0.209 |
4.179 |
0.251 |
4.151 |
0.218 |
3.723 |
4.579 |
0 |
| Controle |
|
|
|
1st quintile |
32 |
0.562 |
0.089 |
2.781 |
0.341 |
4.566 |
0.717 |
3.155 |
5.978 |
32 |
0.562 |
0.089 |
2.781 |
0.341 |
4.566 |
0.717 |
3.155 |
5.978 |
0 |
| Controle |
|
|
|
2nd quintile |
50 |
2.600 |
0.070 |
2.940 |
0.292 |
3.586 |
0.371 |
2.854 |
4.317 |
50 |
2.600 |
0.070 |
2.940 |
0.292 |
3.586 |
0.371 |
2.854 |
4.317 |
0 |
| Controle |
|
|
|
3rd quintile |
71 |
4.789 |
0.094 |
4.070 |
0.240 |
3.492 |
0.319 |
2.863 |
4.121 |
71 |
4.789 |
0.094 |
4.070 |
0.240 |
3.492 |
0.319 |
2.863 |
4.121 |
0 |
| Controle |
|
|
|
4th quintile |
17 |
7.471 |
0.125 |
6.118 |
0.542 |
4.039 |
0.878 |
2.311 |
5.766 |
17 |
7.471 |
0.125 |
6.118 |
0.542 |
4.039 |
0.878 |
2.311 |
5.766 |
0 |
| Experimental |
|
|
|
1st quintile |
15 |
0.667 |
0.126 |
3.467 |
0.533 |
5.194 |
0.805 |
3.610 |
6.778 |
15 |
0.667 |
0.126 |
3.467 |
0.533 |
5.194 |
0.805 |
3.610 |
6.778 |
0 |
| Experimental |
|
|
|
2nd quintile |
37 |
2.459 |
0.083 |
3.919 |
0.341 |
4.643 |
0.427 |
3.804 |
5.483 |
37 |
2.459 |
0.083 |
3.919 |
0.341 |
4.643 |
0.427 |
3.804 |
5.483 |
0 |
| Experimental |
|
|
|
3rd quintile |
65 |
4.785 |
0.094 |
3.969 |
0.284 |
3.393 |
0.328 |
2.747 |
4.038 |
65 |
4.785 |
0.094 |
3.969 |
0.284 |
3.393 |
0.328 |
2.747 |
4.038 |
0 |
| Experimental |
|
|
|
4th quintile |
14 |
7.357 |
0.133 |
5.929 |
0.722 |
3.913 |
0.892 |
2.157 |
5.669 |
14 |
7.357 |
0.133 |
5.929 |
0.722 |
3.913 |
0.892 |
2.157 |
5.669 |
0 |